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The support of a flow $x$ in a network is the subdigraph induced by the arcs $uv$ for which $x(uv)>0$. We discuss a number of results on flows in networks where we put certain restrictions on structure of the support of the flow. Many of…

Discrete Mathematics · Computer Science 2024-05-16 Stéphane Bessy , Jørgen Bang-Jensen , Lucas Picasarri-Arrieta

We develop efficient algorithms for a fundamental network design problem arising in potential-based flow models, which are central to many energy transport networks (e.g., hydrogen and electricity). In contrast to classical network flow…

Discrete Mathematics · Computer Science 2026-04-30 Max Klimm , Marc E. Pfetsch , Martin Skutella , Lea Strubberg

Klinz and Woeginger (1995) prove that the minimum cost quickest flow problem is NP-hard. On the other hand, the quickest minimum cost flow problem can be solved efficiently via a straightforward reduction to the quickest flow problem…

Discrete Mathematics · Computer Science 2023-03-07 Martin Skutella

Nash flows over time describe the behavior of selfish users eager to reach their destination as early as possible while traveling along the arcs of a network with capacities and transit times. Throughout the past decade, they have been…

Computer Science and Game Theory · Computer Science 2020-10-06 Leon Sering , Martin Skutella

Flows over time have received substantial attention from both an optimization and (more recently) a game-theoretic perspective. In this model, each arc has an associated delay for traversing the arc, and a bound on the rate of flow entering…

Data Structures and Algorithms · Computer Science 2019-12-03 Dario Frascaria , Neil Olver

We study the robust maximum flow problem and the robust maximum flow over time problem where a given number of arcs $\Gamma$ may fail or may be delayed. Two prominent models have been introduced for these problems: either one assigns flow…

Optimization and Control · Mathematics 2022-02-23 Christian Biefel , Martina Kuchlbauer , Frauke Liers , Lisa Waldmüller

Besides the classical offline setup of machine learning, stream learning constitutes a well-established setup where data arrives over time in potentially non-stationary environments. Concept drift, the phenomenon that the underlying…

Machine Learning · Computer Science 2024-12-13 Fabian Hinder , Valerie Vaquet , David Komnick , Barbara Hammer

Many real-world phenomena are best represented as interaction networks with dynamic structures (e.g., transaction networks, social networks, traffic networks). Interaction networks capture flow of data which is transferred between their…

Social and Information Networks · Computer Science 2018-10-22 Chrysanthi Kosyfaki , Nikos Mamoulis , Evaggelia Pitoura , Panayiotis Tsaparas

The complexity class NP of decision problems that can be solved nondeterministically in polynomial time is of great theoretical and practical importance where the notion of polynomial-time reductions between NP-problems is a key concept for…

Computational Complexity · Computer Science 2022-12-23 Hans-Jörg Kreowski , Sabine Kuske , Aaron Lye , Aljoscha Windhorst

I introduce a new approach to the maximum flow problem by a simple algorithm with a slightly better runtime. This approach is based on sorting arcs insight of vertices on a residual graph. This new approach leads to an O(mn^0.5) time bound…

Data Structures and Algorithms · Computer Science 2013-02-14 Björn Hlava

The problem of travel time estimation is widely considered as the fundamental challenge of modern logistics. The complex nature of interconnections between spatial aspects of roads and temporal dynamics of ground transport still preserves…

Machine Learning · Computer Science 2022-07-14 Natalia Semenova , Vadim Porvatov , Vladislav Tishin , Artyom Sosedka , Vladislav Zamkovoy

We consider the routing flow shop problem with two machines on an asymmetric network. For this problem we discuss properties of an optimal schedule and present a polynomial time algorithm assuming the number of nodes of the network to be…

Discrete Mathematics · Computer Science 2020-05-14 Ilya Chernykh , Alexander Kononov , Sergey Sevastyanov

The multicommodity flow problem is NP-hard already for two commodities over bipartite graphs. Nonetheless, using our recent theory of n-fold integer programming and extensions developed herein, we are able to establish the surprising…

Combinatorics · Mathematics 2011-01-18 Raymond Hemmecke , Shmuel Onn , Robert Weismantel

We describe a simple deterministic near-linear time approximation scheme for uncapacitated minimum cost flow in undirected graphs with real edge weights, a problem also known as transshipment. Specifically, our algorithm takes as input a…

Data Structures and Algorithms · Computer Science 2024-06-27 Emily Fox

In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between…

Fluid Dynamics · Physics 2016-04-26 Anna M. Lieb , Chris H. Rycroft , Jon Wilkening

Transshipment problem is one of the basic operational research problems. In this paper, our first work is to develop a biologically inspired mathematical model for a dynamical system, which is first used to solve minimum cost flow problem.…

Systems and Control · Computer Science 2014-01-13 Cai Gao , Chao Yan , Daijun Wei , Yong Hu , Sankaran Mahadevan , Yong Deng

Network flow interdiction analysis studies by how much the value of a maximum flow in a network can be diminished by removing components of the network constrained to some budget. Although this problem is strongly NP-complete on general…

Discrete Mathematics · Computer Science 2008-01-14 Rico Zenklusen

We investigate the electrical current and flow (number of parallel paths) between two sets of n sources and n sinks in complex networks. We derive analytical formulas for the average current and flow as a function of n. We show that for…

Discrete Mathematics · Computer Science 2008-11-17 Shai Carmi , Zhenhua Wu , Shlomo Havlin , H. Eugene Stanley

Temporal networks, defined as sequences of time-aggregated adjacency matrices, sample latent graph dynamics and trace trajectories in graph space. By interpreting each adjacency matrix as a different time snapshot of a scalar field,…

Data Analysis, Statistics and Probability · Physics 2026-02-27 Lucas Lacasa

We present elements of a typing theory for flow networks, where "types", "typings", and "type inference" are formulated in terms of familiar notions from polyhedral analysis and convex optimization. Based on this typing theory, we develop…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-05-22 Assaf Kfoury